gemini
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git-master
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git-orchestrator
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goals
Optimize prompts via process goals (controllable behavioral instructions) rather than outcome goals (sparse end-result demands). Grounded in sports psychology meta-analysis showing process goals (d=1.36) vastly outperform outcome goals (d=0.09). Use when designing prompts, optimizing LLM steering, implementing CoT/decomposition patterns, or building automatic prompt optimization pipelines. Instantiates surrogate loss paradigm for discrete prompt space.
graph
Use when extracting entities and relationships, building ontologies, compressing large graphs, or analyzing knowledge structures - provides structural equivalence-based compression achieving 57-95% size reduction, k-bisimulation summarization, categorical quotient constructions, and metagraph hierarchical modeling with scale-invariant properties. Supports recursive refinement through graph topology metrics including |R|/|E| ratios and automorphism analysis.
grounding-router
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hierarchical-reasoning
Implements sophisticated multi-level reasoning for complex problems requiring strategic planning, tactical approach design, and operational execution. Use for problems needing deep analysis, multi-step reasoning, systematic decomposition from first principles to implementation, convergence-aware iterative refinement, or uncertainty quantification across abstraction levels.
hkgb
This skill should be used when building hybrid Knowledge Graphs that integrate structured data (CSV, databases) with automatically extracted entities from unstructured documents (PDFs, text). The pattern establishes a reliable join key between domain graphs and lexical graphs, enabling GraphRAG, document ingestion with metadata enrichment, and Knowledge Graph construction from heterogeneous sources using neo4j-graphrag SimpleKGPipeline.
infranodus-orchestrator
Orchestrates complex knowledge graph analysis workflows using InfraNodus MCP tools integrated with hierarchical-reasoning, knowledge-graph, and obsidian-markdown skills. Use when tasks involve text network analysis, content gap detection, research question generation, SEO optimization, comparative text analysis, or Google search intelligence requiring multi-step analytical workflows with local documentation.
infrastructure-router
Routes infrastructure, MCP, and tooling tasks. Triggers on mcp, server, deploy, orchestrate, pipeline, tools, docker, ci/cd, hook, config, setup.
internal-comms
A set of resources to help me write all kinds of internal communications, using the formats that my company likes to use. Claude should use this skill whenever asked to write some sort of internal communications (status reports, leadership updates, 3P updates, company newsletters, FAQs, incident reports, project updates, etc.).
kaizen
Use when Code implementation and refactoring, architecturing or designing systems, process and workflow improvements, error handling and validation. Provide techniques to avoid over-engineering and apply iterative improvements.
Knowledge Graph Builder
Design and build knowledge graphs. Use when modeling complex relationships, building semantic search, or creating knowledge bases. Covers schema design, entity relationships, and graph database selection.
knowledge-orchestrator
Intelligently coordinates obsidian-markdown, hierarchical-reasoning, and knowledge-graph skills through automatic skill selection, context-aware routing, and hybrid MCP integration. Use when tasks involve knowledge base construction, research synthesis, documentation generation, or learning workflows that could benefit from multi-skill composition. Activates automatically to evaluate whether specialized skills should handle the request.
Knowledge Transfer
Progressive learning methodology for structured onboarding using time-boxed learning paths (Day-1, Week-1, Month-1), validation checkpoints, and scaffolding principles. Use when onboarding new contributors, reducing ramp-up time from weeks to days, creating self-service learning paths, systematizing ad-hoc knowledge sharing, or building institutional knowledge preservation. Provides 3 learning path templates (Day-1: 4-8h setup→contribution, Week-1: 20-40h architecture→feature, Month-1: 40-160h expertise→mentoring), progressive disclosure pattern, validation checkpoint principle, module mastery best practice. Validated with 3-8x onboarding speedup (structured vs. unstructured), 95%+ transferability to any software project (Go, Rust, Python, TypeScript). Learning theory principles applied: progressive disclosure, scaffolding, validation checkpoints, time-boxing.
Lambda
Universal transformation λ(ο,K).τ with recursive self-improvement. USE WHEN routing reasoning, validating knowledge graphs, preparing CICM/ANZCA examinations, or when self-improvement of reasoning/architecture/context is required. Routes queries through R0-R3 complexity pipelines, validates topology (η≥target) and governance (KROG), emits per style (Φ), and compounds learnings into knowledge K. Triggers on complexity assessment, multi-step reasoning, examination mode, or /λ invocation.
leann
Local RAG indexing with 97% storage reduction via anchor-based lazy recomputation. Graph-based selective embedding storage for memory-efficient semantic code search.
limitless-cli
CLI for Limitless.ai Pendant with lifelog management, FalkorDBLite semantic graph, vector embeddings, and DAG pipelines. Use for personal memory queries, semantic search across lifelogs/chats/persons/topics, entity extraction, and knowledge graph operations. Triggers include "lifelog", "pendant", "limitless", "personal memory", "semantic search", "graph query", "extraction".
lindy-expert
Lindy is an AI agent creation and management platform focused on business process automation. It enables organizations to build sophisticated AI-powered assistants that can handle communication across...
maker-framework
Orchestrate reliable multi-agent reasoning using MAKER (Maximal Agentic Knowledge Engine for Reasoning). Implements three-pillar architecture for transforming probabilistic LLM outputs into deterministic, verifiable results. Use when tasks require high reliability, parallel consensus voting, or systematic error detection. Triggers include reliability-critical tasks, multi-step reasoning chains, consensus-based verification, parallel agent execution, or explicit MAKER invocation.
mcp-skillset-workflows
Nuanced multi-skill orchestration patterns combining debugging, TDD, parallel agents, and root-cause tracing for complex software development
mcp_agent_mail
FastMCP agent-to-agent communication system with messaging, file reservations, and multi-repo coordination
mega
Maximally Endowed Graph Architecture — λ-calculus over bounded n-SuperHyperGraphs with grounded uncertainty, conditional self-duality, and autopoietic refinement. Use when (1) simple graphs insufficient (η<2), (2) multi-scale reasoning required, (3) uncertainty is structured not stochastic, (4) knowledge must self-refactor. Pareto-governed: complexity added only when simpler structures fail validation.
meta-router
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Methodology Bootstrapping
Apply Bootstrapped AI Methodology Engineering (BAIME) to develop project-specific methodologies through systematic Observe-Codify-Automate cycles with dual-layer value functions (instance quality + methodology quality). Use when creating testing strategies, CI/CD pipelines, error handling patterns, observability systems, or any reusable development methodology. Provides structured framework with convergence criteria, agent coordination, and empirical validation. Validated in 8 experiments with 100% success rate, 4.9 avg iterations, 10-50x speedup vs ad-hoc. Works for testing, CI/CD, error recovery, dependency management, documentation systems, knowledge transfer, technical debt, cross-cutting concerns.
model-enhancement-servers
Guide for creating MCP servers that enhance LLM reasoning through structured processes, persistence, and workflow guidance. Use when building MCP servers for structured thinking, journaling, memory systems, or other cognitive enhancement patterns.
multi-agent-coordination
Automatically invoked when peer agents are detected in the same project. Establishes coordination protocols, file reservations, and message-based collaboration. Triggers on SessionStart when other agents exist in the project.
network-meta-analysis-appraisal
Systematically appraise network meta-analysis papers using integrated 200-point checklist (PRISMA-NMA, NICE DSU TSD 7, ISPOR-AMCP-NPC, CINeMA) with triple-validation methodology, automated PDF extraction, semantic evidence matching, and concordance analysis. Use when evaluating NMA quality for peer review, guideline development, HTA, or reimbursement decisions.
non-linear
Uncertainty-aware non-linear reasoning system with recursive subagent orchestration. Triggers for complex reasoning, research, multi-domain synthesis, or when explicit commands `/nlr`, `/reason`, `/think-deep` are used. Integrates think skill (reasoning), agent-core skill (acting), and MCP tools (infranodus, exa, scholar-gateway) in recursive think→act→observe loops. Uses coding sandbox for execution validation and maintains deliberate noisiness via NoisyGraph scaffold. Supports `/compact` mode for abbreviated outputs and `/semantic` mode for rich exploration.
Observability Instrumentation
Comprehensive observability methodology implementing three pillars (logs, metrics, traces) with structured logging using Go slog, Prometheus-style metrics, and distributed tracing patterns. Use when adding observability from scratch, logs unstructured or inadequate, no metrics collection, debugging production issues difficult, or need performance monitoring. Provides structured logging patterns (contextual logging, log levels DEBUG/INFO/WARN/ERROR, request ID propagation), metrics instrumentation (counter/gauge/histogram patterns, Prometheus exposition), tracing setup (span creation, context propagation, sampling strategies), and Go slog best practices (JSON formatting, attribute management, handler configuration). Validated in meta-cc with 23-46x speedup vs ad-hoc logging, 90-95% transferability across languages (slog specific to Go but patterns universal).
obsidian-batch
Batch processing for Obsidian vaults: bulk tag normalization, wikilink extraction/fixing, frontmatter edits, vault analysis, and migration workflows. Use when asked to analyze or modify many notes in an Obsidian vault at scale, or to script/automate vault-wide changes.
obsidian-data-importer
Transform structured external data (CSV/JSON) into Obsidian's linked knowledge system while preserving semantic relationships, optimizing graph structure, and preventing data corruption through systematic validation and YAML-safe template generation.Enables seamless knowledge transfer from databases, spreadsheets, and APIs into personal knowledge management, maintaining referential integrity and facilitating emergence of insights through networked thought.
obsidian-developer
Expert guide for inspecting and automating Obsidian using the obsidian-devtools MCP server.
obsidian-devtools
Inspect and automate Obsidian using Chrome DevTools Protocol
obsidian-markdown
Enforces Obsidian-flavored Markdown syntax when creating any .md file, providing templates, validation, and pattern enforcement for YAML frontmatter, wikilinks, callouts, dataview queries, tables, mermaid diagrams, templater syntax, canvas/JSONCanvas, bases, and footnotes. Activates automatically unless explicitly stated otherwise.
obsidian-process
This skill should be used when batch processing Obsidian markdown vaults. Handles wikilink extraction, tag normalization, frontmatter CRUD operations, and vault analysis. Use for vault-wide transformations, link auditing, tag standardization, metadata management, and migration workflows. Integrates with obsidian-markdown for syntax validation and obsidian-data-importer for structured imports.
obsidian
This skill should be used when working with Obsidian files including .md notes with wikilinks/callouts/properties, .base database views with filters/formulas, or .canvas visual diagrams. Routes to specialized sub-skills based on file type and task context. Features self-iterative learning that improves with use.
ontolog
Holarchic reasoning framework implementing λ-calculus over simplicial complexes. Entities (ο) transform through operations (λ) toward terminals (τ) via the universal form λο.τ. Persistent homology captures multi-scale structure; sheaf theory ensures local-to-global consistency. Use when knowledge requires: (1) homoiconic self-reference where structure mirrors content, (2) scale-invariant holonic decomposition, (3) topological invariants preserved across transformations, or (4) formal Lex-style axiom systems over property graphs.
orchestrator
Meta-skill orchestrating 7 atomic skills into unified workflows for intelligent development
osgrep
Semantic NLP-based code search using neural embeddings and hybrid ranking
pageindex-rag
Use this skill whenever the user wants high-quality semantic retrieval, cross-textbook synthesis, reference verification, or authority-ranked question answering over the PageIndex corpus. Trigger on requests like "find which texts cover X", "compare sources", "retrieve semantically", "verify this claim from my textbooks", "build a reading list from the corpus", "cluster by topic", or any PageIndex question where naïve filename search would miss relevant material. This skill teaches a state-of-the-art hybrid retrieval workflow over the PageIndex catalog and live MCP extraction surface.
parallel-debug-orchestrator
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pex-cli
Primary Exam (PEX) Knowledge Graph CLI for CICM/ANZCA exam preparation. Semantic search over Learning Outcomes, SAQs, concepts, and topics. Use for exam study, prerequisite chains, learning paths, and higher-order concept connections.
process
Batch processing for Obsidian vaults: bulk tag normalization, wikilink extraction/fixing, frontmatter edits, vault analysis, and migration workflows. Use when asked to analyze or modify many notes in an Obsidian vault at scale, or to script/automate vault-wide changes.
prompting
Prompt engineering standards and context engineering principles for AI agents based on Anthropic best practices. Covers clarity, structure, progressive discovery, and optimization for signal-to-noise ratio.
quantitative-physiology
This skill should be used when calculating physiological parameters, modeling membrane transport, analyzing cardiovascular hemodynamics, computing renal clearance, simulating action potentials, or explaining quantitative relationships in any human physiological system. Use for physiology homework, medical calculations, computational biology modeling, and pharmacokinetic analysis.
ralph-graceful-exit
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ralph-invoke
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ralph-prd
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Rapid Convergence
Achieve 3-4 iteration methodology convergence (vs standard 5-7) when clear baseline metrics exist, domain scope is focused, and direct validation is possible. Use when you have V_meta baseline ≥0.40, quantifiable success criteria, retrospective validation data, and generic agents are sufficient. Enables 40-60% time reduction (10-15 hours vs 20-30 hours) without sacrificing quality. Prediction model helps estimate iteration count during experiment planning. Validated in error recovery (3 iterations, 10 hours, V_instance=0.83, V_meta=0.85).
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